华南理工大学学报（自然科学版）2025，Vol.53Issue(8)：50-60,11.DOI:10.12141/j.issn.1000-565X.240437
基于ExiD的高速公路合流汇入特性与安全性研究
An Investigation into Expressway Merging Behavior and Safety Based on ExiD Data
摘要
Abstract
Expressway merging areas are characterized by frequent lane changes,complex driving environments,and intense traffic conflicts,making them high-risk zones for traffic accidents.Accurately understanding the rela-tionship between vehicle operating status and traffic safety in these merging areas can provide a foundation for real-time accident risk prediction and the development of effective traffic safety control strategies.This study is based on vehicle trajectory data from the German ExiD dataset.By analyzing the changes in the relative positions of main-line outer-lane vehicles during the process from a vehicle entering the acceleration lane to merging into the main-line,the merging patterns in expressway merging areas were classified.To systematically describe the safety risks associated with merging,this study introduced the Time to Collision theory and developed a risk representation framework.This framework includes two levels of indicators:(1)a merging moment risk indicator based on two-dimensional TTC,which evaluates potential conflict at the moment of merging;and(2)a merging process risk indica-tor based on collision exposure time,which reflects the accumulated risk throughout the merging process.For model development,four machine learning algorithms—XGBoost,LightGBM,GBDT,and Random Forest—were used to build a classification model for merging risk.In addition,SHAP was applied to interpret the model and ana-lyzed the key factors influencing merging risk.Experimental results show that the XGBoost-based risk identifica-tion model for expressway merging areas outperforms other models,achieving an overall accuracy of 95.52%.It also demonstrates superior performance in terms of accuracy,precision,recall,and F1-score.Furthermore,compari-son among models indicates that incorporating merging duration and urgency significantly improves risk identifica-tion accuracy.SHAP analysis further reveals that merging risk is closely related to several factors,including the ave-rage and maximum speed differences with the leading vehicle on the mainline,the average distance to the leading vehicle,merging duration,the standard deviation of longitudinal acceleration during merging,and the speed of the merging vehicle.关键词
交通安全/高速公路合流区/交通冲突/风险识别/机器学习Key words
traffic safety/expressway merging areas/traffic conflicts/risk identification/machine learning分类
交通工程引用本文复制引用
温惠英,黄坤火,陈喆,赵胜,胡宇晴,黄俊达..基于ExiD的高速公路合流汇入特性与安全性研究[J].华南理工大学学报（自然科学版）,2025,53(8):50-60,11.基金项目
国家自然科学基金项目(52372329)Supported by the National Natural Science Foundation of China(52372329) （52372329）
